

    \filetitle{filter}{Filter data using a VAR model}{VAR/filter}

	\paragraph{Syntax}

\begin{verbatim}
[V,Outp] = filter(V,Inp,Range,...)
\end{verbatim}

\paragraph{Input arguments}

\begin{itemize}
\item
  \texttt{V} {[} VAR {]} - Input VAR object.
\item
  \texttt{Inp} {[} struct {]} - Input database from which initial
  condition will be read.
\item
  \texttt{Range} {[} numeric {]} - Forecast range.
\end{itemize}

\paragraph{Output arguments}

\begin{itemize}
\item
  \texttt{V} {[} VAR {]} - Output VAR object.
\item
  \texttt{Outp} {[} struct {]} - Output database with prediction and/or
  smoothed data.
\end{itemize}

\paragraph{Options}

\begin{itemize}
\item
  \texttt{'cross='} {[} numeric \textbar{} \emph{\texttt{1}} {]} -
  Multiply the off-diagonal elements of the covariance matrix
  (cross-covariances) by this factor; \texttt{'cross='} must be equal to
  or smaller than \texttt{1}.
\item
  \texttt{'deviation='} {[} \texttt{true} \textbar{}
  \emph{\texttt{false}} {]} - Both input and output data are deviations
  from the unconditional mean.
\item
  \texttt{'meanOnly='} {[} \texttt{true} \textbar{}
  \emph{\texttt{false}} {]} - Return a plain database with mean
  forecasts only.
\item
  \texttt{'omega='} {[} numeric \textbar{} \emph{empty} {]} - Modify the
  covariance matrix of residuals for this run of the filter.
\end{itemize}

\paragraph{Description}

\paragraph{Example}


